Tree Canopy and it’s Impact on the Urban Heat Effect
Authors
Affiliation
Eleanor Lindsey
Colorado State University
Clara Jordan
Colorado State University
Sierra Champion
Colorado State University
The Relationship of Tree Canopy to Urban Heat Effect and Inequality: A Case Study
ABSTRACT
This study investigates the relationship between tree canopy cover, urban temperature, and income levels in Los Angeles, California, with a focus on the Urban Heat Island (UHI) effect. As cities like Los Angeles continue to expand and global temperatures rise, urban areas are experiencing higher heat exposure, particularly in neighborhoods with limited green space. We aimed to test two main hypotheses: (1) that lower tree canopy coverage would be associated with higher temperatures, and (2) that lower canopy coverage would be correlated with lower income, based on the “luxury effect” described in previous research. Using data from 2003 to 2023, including average annual temperatures, tree canopy loss, and 2023 census tract median incomes, we conducted visual and statistical analyses to explore these relationships. While visualizations suggested a strong association between canopy loss and temperature, the Pearson correlation test did not yield statistically significant results. Additionally, our analysis found no measurable correlation between tree canopy and income, although this may be due to limitations in the data structure. Despite these challenges, our findings reinforce the importance of urban tree canopies in regulating temperature and highlight the need for more targeted, high-resolution data to understand environmental inequalities in urban heat exposure fully.
INTRODUCTION & HYPOTHESIS
Due to the effects of climate change, large cities like Los Angeles, California, are expected to see regularly increasing temperatures. When looking at a heat map, an odd phenomenon shows higher temperatures within city limits than in rural areas on the border. This unequal heating is called the Urban Heat Island (UHI) effect (HeatIslandHealthEffects?). This is due to reduced vegetation cover, increased concrete, disruptive buildings, and human activities. This effect is projected to worsen as more of the world’s population migrates to urban environments, cities continue to grow, and global temperatures rise (HeatIslandTrends?) Areas already impacted by the UHI effect will “bear the brunt” of intensified heat waves brought on by climate change (HeatIslandTrends?).
From the 1980s onward, environmental movements began to advocate for increased green space and sustainability in urban planning. Initiatives promoting tree canopy restoration, urban greening, and park creation have been prioritized to mitigate the impacts of climate change and improve public health. Notably, the city has recognized the need to address the disproportionate effects of urban heat on low-income communities. However, despite these efforts, many neighborhoods still struggle with heat exposure and lack adequate vegetation, emphasizing the need for continued research and policy reform to ensure equitable access to urban green spaces.
During this period, a growing awareness of environmental issues began to emerge. Land-use planning and urban development often neglect the integration of green spaces, leading to stark inequalities within the city. Areas predominantly inhabited by low-income communities and people of color were typically under served regarding infrastructure and green spaces, resulting in disparities in health outcomes and environmental benefits compared to wealthier neighborhoods (UrbanGreenSpaceInequality?).
Even before considering climate change, the urban heat island effect can have severe impacts, including changing urban ecosystems and biodiversity and exacerbating human health issues. Increasing urban temperatures can lead to health impacts, including increased exposure to extreme heat, air pollution, heat stroke, cardiorespiratory mortality, kidney disease, and mental illness, among others (HeatIslandHealthEffects?).
One of the primary benefits of urban green spaces is their ability to cool the surrounding environment. Trees and vegetation play a vital role in reducing temperatures through evapotranspiration, where water is absorbed by the roots and released as water vapor from the leaves. This natural cooling effect can significantly lower urban temperatures, helping to combat the extreme heat conditions associated with the Urban Heat Island (UHI) effect. Research shows that areas with denser tree canopies can experience 5-10 degrees Fahrenheit temperature reductions compared to surrounding asphalt or concrete regions (CoolingEffectsOfTrees?).
Additionally, urban green spaces contribute to improved air quality. Trees act as natural air filters by absorbing pollutants and providing oxygen. Green spaces have been linked to reductions in air pollutants like carbon dioxide, nitrogen dioxide, and particulate matter, thereby promoting healthier urban environments (UrbanGreenSpacesAirQuality?). Studies indicate that residents living near green spaces report better physical and mental health outcomes, likely due to increased opportunities for physical activity, social interaction, and psychological restoration provided by nature (HealthBenefitsOfGreenSpaces?).
Los Angeles is a prime example of the UHI effect as it is a large city housing millions of people with a tall, dense skyline. Asphalt and blacktop trap a significant amount of heat, making the temperatures within the five counties that make up the metropolis hotter than those in surrounding rural areas (LAHeatIsland?). In this study, we aim to investigate how the urban heat island effect in Los Angeles is related to tree cover and income. We intend to explore if tree cover and temperature are related, and how this may be spatially associated with income. We were inspired by Schell et al. 2020 b’s thoughts about the luxury effect, the idea that urban biodiversity is positively correlated with neighborhood wealth (RacismInUrbanEnvironments?). This curiosity raised questions like: How will climate change exacerbate urban heat? Can we identify neighborhoods in Los Angeles at higher risk of urban heat health impacts? To investigate this question, the objective of our study was to answer the following question: Does decreased tree canopy contribute to increased urban temperatures and correlate with median income in Los Angeles? Our hypotheses include:
As tree canopy decreases, the mean annual temperature will increase because of the urban heat island effect.
As tree canopy decreases, the mean annual income will decrease due to the historical marginalization of low-income home communities in neighborhoods with little green space, per the luxury effect (RacismInUrbanEnvironments?).
To explore these hypotheses, we obtained Los Angeles-centric data on yearly average temperatures in Los Angeles from 2013 - 2023 (TemperatureData?), tree canopy cover loss from 2001 - 2023 (LATreeLoss?), and median income for 2023 (MedianIncomeCensusTract?). With R as our analysis platform, we combined these datasets and visually analyzed the relationships between our three variables using scatter plots.
METHODS
We have compiled data sets on air temperature records, median income, and land cover. Our question pertains to the heat island effect, which is correlated to the change in air temperature within city limits. We are also looking into median income to see how the heat island effect impacts different people. The land cover data is added for further questions about how landforms impact temperature and urban sprawl. To analyze this data, we plan to use Dplyr to clean it, such as selecting, filtering, and omitting NA values. Because we are using census data, we expect different periods for the datasets. The median income data only has a five-year range, while the temperature and land cover data span from 2003 to 2023.
Once our data was cleaned, we joined our data sets (temperature, land cover, and income) using a left join on the “year” column. This was done to create a unified data frame to analyze. One challenge we anticipate is joining these data sets because our data sets focus on a variety of data types, and we may need to create new columns that are common to all data sets so we can execute a join. After analyzing the data frames, we found that each set had a year column that could be used to join. We are operating under the assumption that we have everything we need; however, once our datasets are joined, we may need supplemental data on population density to contextualize heat island increases and income distribution.
This joined data frame was then analyzed to answer our question: Does decreased tree canopy contribute to increased urban temperatures and correlate with median income? To answer this question, wel:
Tested for correlation between tree canopy and mean annual temperature in Los Angeles using the correlation function. This corresponded to Hypothesis A (see Introduction).
Tested for correlation between tree canopy and mean annual income in Los Angeles using the correlation function. This corresponded to Hypothesis B (see Introduction).
These methods helped us answer our question by revealing potential relationships between tree canopy, mean annual temperature, and income. This allowed us to draw conclusions about which communities in Los Angeles should implement adaptation measures as climate change increases temperatures and exacerbates the urban heat island effect, along with its negative health impacts.
RESULTS
This study found that as the tree canopy decreases, mean annual temperature will increase, and discerned no relationship between tree canopy and median income. To test Hypothesis A, as tree canopy decreases, the mean annual temperature will increase because of the urban heat island effect, we first explored a comparison between urban and suburban temperature data from downtown Los Angeles and Malibu Hills, respectively. This comparison revealed a stark contrast between suburban and urban temperatures across the twenty-year time series from 2003 to 2023. Consistently throughout this time series, the Malibu Hills temperature records were lower than those for downtown Los Angeles, as summarized in Figure 1 below.
Figure 1:
library(here)
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temp_data1<-read_csv(here("data/2003-2005 Temperature Data(in).csv"))
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temp_data2<-read_csv(here("data/2006-2012 Temperature Data(in).csv"))
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temp_data3<-read_csv(here("data/2013-2019 Temperature Data(in).csv"))
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temp_data4<-read_csv(here("data/2020-2023 Temperature Data(in).csv"))
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downtown_stations <-c("LOS ANGELES DOWNTOWN USC, CA US", "MALIBU HILLS CALIFORNIA, CA US")temperature_data_filtered <- temperature_data %>%filter(NAME %in% downtown_stations)station_labels<-c("LOS ANGELES DOWNTOWN USC, CA US"="Downtown LA","MALIBU HILLS CALIFORNIA, CA US"='Malibu Hills' )Temperature_plot <-ggplot(temperature_data_filtered, aes(x = Year, y = TAVG, color = NAME)) +geom_line() +scale_x_continuous(breaks =seq(2003, 2023, by =4), labels =seq(2003, 2023, by =4)) +scale_color_manual(values =c("dodgerblue", "firebrick"), labels = station_labels) +labs(title ="Average Temperature in Los Angeles Downtown Stations",x ="Year",y ="Temperature (°F)" ) +theme_minimal(base_size =14)ggplotly(Temperature_plot)
In Figure 1 the temperature data is categorized by a yearly average temperature in LA in degrees Fahrenheit. These results confirm the existence of the UHE, characterized by higher and less variable temperatures in the urban environment than the suburban environment. This study then incorporated tree canopy loss as a variable influencing temperature. Figure 2 visualizes trends in tree canopy loss in Los Angeles over the same time series considered for temperature data. We can see continual tree canopy loss over the twenty years considered, characteristic of urban environments which continue to develop as population increases over time. Spikes indicate periods of elevated tree canopy loss, such as in 2009 and 2020.
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Tree_loss_plot<-ggplot(tree_canopy_data, aes(x=Year, y=Tree_loss_ha))+geom_area(fill="forestgreen",color="forestgreen")+labs(x="Year", y="Tree Canopy Loss (ha)", title="Tree Canopy Loss in LA per Year")Tree_loss_plot
Given the hypothesized relationship between tree canopy loss and temperature due to the UHE, it is no surprise that this study also found a very strong visual correlation between these two variables. As seen below in Figure 3, temperature trends follow those of tree canopy loss in Los Angeles. While the spikes and dips are not to the same order of magnitude between these two variables, it is evident that increases in tree canopy cover loss impact temperature. For example, in 2009, there was a large decrease in tree canopy cover of over 20,000 hectares. After this spike, temperature readings in both downtown Los Angeles and Malibu Hills increase steadily for approximately the next five years. It is important to note that throughout these changes in tree canopy cover that the variation in Malibu Hills temperature records is less than that of downtown Los Angeles, further demonstrating the regulatory and cooling impact of tree canopy in developed environments.
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combined_Temp_tree_data<-left_join(tree_canopy_data,temperature_data, by='Year')%>%rename('Temp_average'='TAVG', 'Station_name'='NAME')combined_income_tree_temp <-cross_join(combined_Temp_tree_data, median_income_data)combined_income_tree_temp <- combined_income_tree_temp[-c(1:4990), ]station_labels <-c("LOS ANGELES DOWNTOWN USC, CA US"="Downtown LA","MALIBU HILLS CALIFORNIA, CA US"="Malibu Hills")filtered_data_combined <- combined_income_tree_temp %>%filter(Station_name %in%c("LOS ANGELES DOWNTOWN USC, CA US", "MALIBU HILLS CALIFORNIA, CA US"))scale_factor <-max(combined_income_tree_temp$Tree_loss_ha, na.rm =TRUE) /max(combined_income_tree_temp$Temp_average, na.rm =TRUE)# Plotcombined_plot <-ggplot(filtered_data_combined, aes(x = Year)) +# Tree loss linegeom_line(aes(y = Tree_loss_ha), color ="forestgreen", size =1) +# Temperature lines by stationgeom_line(aes(y = Temp_average * scale_factor, color = Station_name), size =1) +scale_x_continuous(breaks =seq(2003, 2023, by =2),labels =seq(2003, 2023, by =2) ) +scale_y_continuous(name ="Tree Canopy Loss (ha)",sec.axis =sec_axis(~./scale_factor, name ="Average Temperature (°F)") ) +scale_color_manual(values =c("LOS ANGELES DOWNTOWN USC, CA US"="dodgerblue","MALIBU HILLS CALIFORNIA, CA US"="firebrick"),labels = station_labels,name ="Weather Station" ) +theme_minimal() +theme(axis.title.y.left =element_text(color ="forestgreen"),axis.text.y.left =element_text(color ="forestgreen"),axis.title.y.right =element_text(color ="black"),axis.text.y.right =element_text(color ="black"),legend.position ="bottom" ) +labs(title ="Tree Loss and Average Temperature by Year",x ="Year" )
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
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